Robert Munnoch
Papers
2
Total Citations
37
H-Index
2
About
Robert Munnoch is a researcher specializing in computer vision and robotics, with a primary focus on indoor object recognition for autonomous navigation. His work addresses a critical challenge in mobile robotics: enabling machines to accurately identify and interact with objects in complex indoor environments. Munnoch’s major contributions center on the application of deep learning, particularly convolutional neural networks (CNNs), to improve detection precision and robustness. His most cited paper, “Prior knowledge-based deep learning method for indoor object recognition and application” (2018, 22 citations), introduces a novel approach that integrates prior contextual knowledge to enhance recognition accuracy, moving beyond traditional methods that struggle with unfamiliar or cluttered settings. His earlier work, “Indoor object recognition using pre-trained convolutional neural network” (2017, 15 citations), established a foundational pipeline that leverages transfer learning from pre-trained CNN models, combining public and private datasets to boost performance. Together, these studies have garnered over 37 citations, reflecting their influence on advancing robot perception systems. Munnoch’s research is notable for bridging theoretical deep learning techniques with practical robotic applications, offering scalable solutions for real-world indoor navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Indoor object recognition using pre-trained convolutional neural network15 citations · 2017